Decoding the Black Box: Explainable AI Strategies in Data Engineering Pipelines

Abstract

As Artificial Intelligence (AI) continues to play an integral role in data engineering pipelines, the challenge of interpreting and understanding complex AI models persists. This paper delves into the critical domain of Explainable AI (XAI) strategies and their integration into data engineering pipelines, aiming to demystify the "black box" nature of advanced machine learning algorithms. Our research focuses on elucidating various XAI techniques, including feature importance analysis, model-agnostic methods, and interpretable machine learning models. By incorporating these strategies into data engineering workflows, we enhance the transparency and comprehensibility of AI models, fostering trust and facilitating informed decision-making. Through case studies and practical implementations, we illustrate the impact of XAI on diverse data engineering scenarios. The paper emphasizes the significance of not only achieving high predictive accuracy but also ensuring the interpretability of AI models for stakeholders and end-users. We explore the trade-offs between model complexity and interpretability, providing insights into selecting the most suitable XAI strategy based on specific use cases.